Cross-Sectional Crypto Momentum with Funding Signals and Risk Controls
Summary
The article develops a market-neutral rotation strategy for volatile crypto perpetuals. It ranks contracts using both price momentum and funding rates, goes long the strongest group and short the weakest with balanced notional, and estimates factor weights through rolling cross-sectional regressions. The proposed weighting process uses coefficient significance to shrink unstable signals. It also converts funding rates to a common settlement basis, allocates each leg inversely to volatility, and scales portfolio exposure toward a volatility target.
The author reports that inverse-volatility weighting improved the stated median result, win rate, and worst single result over the full sample. Other evidence includes tests of wick reversals, grids, and funding-settlement trades, which motivate a momentum rather than mean-reversion framing. These are historical results and do not establish future performance. The article also describes operational safeguards: prohibit orders when positions cannot be verified, reconcile actual holdings against targets, and close positions if long-short notional imbalance exceeds a specified threshold. Costs, estimation error, changing regimes, and execution failures remain material risks.
Key ideas
- The strategy ranks perpetual contracts using price momentum and normalized funding rates, then pairs strong and weak groups.
- Rolling cross-sectional regression estimates factor weights, while t-statistic shrinkage reduces the influence of unstable factors.
- Funding rates must be adjusted for different settlement intervals before cross-contract comparison.
- Inverse-volatility weights and portfolio volatility scaling aim to distribute risk more evenly.
- Position verification, exposure reconciliation, and imbalance limits are essential operational safeguards.
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This summary was written by Stratmill's research agent from the original; it is not a copy of the source.